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Player Position Binary Classification Model 3

Player Position Binary Classification Model

引用

3rd international conference on Computer Applications Technology, CCAT 2024

作者： Morgan, Kayanna A. Godasu, Rajesh Grant, Emanuel S. University of North Dakota School of Electrical Engineering and Computer Science Grand Forks United States

ISBN: (纸本)9798331539795

This paper presents a binary classification model aimed at predicting whether a football player is best suited for an offensive or defensive position based on their skill set. The study addresses the challenges in precisely projecting a player's future potential solely from their skills, a gap in sports analytics and player evaluation research. Leveraging machine learning techniques, specifically neural networks, the model aims to classify players into their optimal position groups, contributing to talent identification and recruitment strategies in football. Evaluation metrics demonstrate the model's ability to accurately predict player positions, with a slight improvement observed in the enhanced model. However, the increase in model complexity does not yield significant performance gains, emphasizing the importance of balancing complexity with performance improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Temperature Prediction for Pune City: machine learning-Enabled Temperature Prediction 3

Advancing Temperature Prediction for Pune City: Machine Lear...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sathe, Ganesh Pathak, Anagha M. Jadkar, Sandesh Bhalla, Vishal Dattangire, Rahul Savitribai Phule Pune University Center for Energy Studies M.S. Pune India School of Artificial Intelligence Coimbatore India Rivier University NH United States

ISBN: (纸本)9798350369083

This study investigates the efficacy of machine learning methodologies in enhancing temperature forecasting accuracy for Pune City, a region characterized by diverse geographical and climatic factors. Leveraging historical weather data, a comprehensive dataset is compiled, encompassing various meteorological variables. Various machine learning regression algorithms is used and evaluated to predict future temperature trends. Despite encountering challenges such as data scarcity and model complexity, the study demonstrates promising results. Random Forest Regressor exhibited the highest accuracy with an average mean absolute error of 0.02, followed closely by Gradient Boosting Regressor and Neural Network regression techniques. The implications of this research are significant, as accurate temperature forecasts are crucial for sectors such as Thermal Energy Storage Management, Refrigeration and Air Conditioning Sector (RAC), agriculture, transportation, and urban planning. By innovatively applying machine learning techniques, this study contributes to advancing weather prediction capabilities, providing valuable insights into temperature dynamics and enhancing preparedness for climate-related events. The novelty and significance lie in addressing unique weather forecasting challenges specific to Pune City, ultimately fostering more efficient and reliable weather prediction systems in urban environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating machine learning and IoT Technologies for Advancements in Solar Energy Systems 3

Integrating Machine Learning and IoT Technologies for Advanc...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Venkatesan, G. Marimuthu, M. Gomathy, V. Saranya, N. Anandaram, Harishchander Arun Kumar, U. Care College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tiruchirappalli620009 India Saranathan College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tiruchirappalli620012 India Kathir College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641062 India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Tamil Nadu Coimbatore India Faculty of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India

ISBN: (纸本)9798350375190

This research focuses on the synergistic application of machine learning and Internet of Things technologies for the improvement of the performance of renewable energy systems based on the example of solar energy conversion. The research covers the aspects of a solar energy conversion system implementation, in which a set of solar panels, a multilevel inverter, and various sensors that monitor solar irradiance, temperature, and voltage parameters have been used. The sensors have an IoT feature of wireless data transfer to a central control unit. machine learning algorithms, in particular, Artificial Neural Networks, Support Vector machines, Decision Trees, and Random Forest, are used to analyze historical and real-time data to provide predictive maintenance, to forecast energy demand of the users, and to detect faults. The control framework of the system uses a Proportional-Integral-Derivative control loop to adjust the output voltage of the multilevel inverter through comparison with the required grid or load demand. The performance of the control shows significant improvements of the conversion efficiency of energy, the precision of voltage regulation, and the capacity for predictive maintenance. Particularly, it is defined that the accuracy of the prediction of the response to maintenance of the ANN algorithm is 96.7% that is the highest among the studied models. As a result, it is defined that renewable systems are dynamic, and adaptive control is essential for their effective functioning taking into consideration different environmental conditions. The results can be useful for the future design and operation of such systems, and it has the potential to create a sustainable and reliable energy supply by improving the efficiency of renewable energy systems. © 2024 IEEE.

关键词： Internet of things

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Research on the Intelligent Question-Answering Model of Electric Power Knowledge Based on Deep learning 3

Research on the Intelligent Question-Answering Model of Elec...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Fu, Hanchen Xu, Jingjing Kang, Naidi Cao, Shilong State Grid Liaoning Marketing Service Center Liaoning Shenyang110168 China

ISBN: (纸本)9798350380989

In the context of digital economy era, power marketing data information is called the barometer of economic operation stability, which to a large extent represents the development trend and situation of social economy[l]. In recent years, with the continuous improvement of the attention of power marketing data, the opening and sharing of power marketing data has become one of the hot social topics[2]. In view of the shortcomings of the current knowledge base intelligent interaction, we put forward intelligent knowledge base model based on deep learning. By joining the BiGRU layer it can be extracted from the BERT output vector sequence up and down the correlation information, real-time update problem dynamic processing scheme of information improves the knowledge base, and accelerates the information transmission and sharing[3]. © 2024 IEEE.

关键词： BERT deep learning model repository

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Automation of data Analysis and Web-Scraping to Value Used Items 3

Automation of Data Analysis and Web-Scraping to Value Used I...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Shriya, Y.S. Babu, Aarav Chandrashekhar, Naren Verma, Siddhant Jagdish Shanthala, P.T. PES University Computer Science Engineering Dept Bangalore India

ISBN: (纸本)9798350372120

Second-hand products such as phones, laptops, car's, etc. make up a substantial part of the market. They aid a sustainable lifestyle by reducing the use of new resources and diminishing waste disposal. With the evolution of second-hand shopping, pricing has become a very tedious and cumbersome process. Traditionally, models are pre-trained on static datasets. With the trends in the market being sporadic, the outcome of such models is greatly affected. One approach to counter this would be to update the dataset periodically, which requires significant resource allocation. This approach is better suited when dealing with a dataset that contains a large range of products together but may prove inefficient when encountering a specific item. Through this study, a pipeline is established that integrates real-time web scraping with machine learning, which is achieved by using the Selenium tool, automating data preparation of the web scraped data, and implementing a machine learning model. The model considers essential item features to provide accurate predictions for specific items queried by a user. This paper also helps us perform a comparative study of various machine learning models that are considered ideal for regression analysis. When dealing with a dataset, limited in size, the need to pick a model that can handle the sparsity in data becomes critical. Tuning the Random Forest model to 30 trees showed the best results when compared to the other regression models. It predicted accurate prices on multiple iterations with varying inputs, whereas the other models did not account for the change in the features. © 2024 IEEE.

关键词： Costs

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Statistical Feature Extraction and Classification of Complex data Based on Deep learning 3

Statistical Feature Extraction and Classification of Complex...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Wu, Dongmei Chengdu College of University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9798350376173

This research is focused on elucidating the extraction of statistical features and the classification methodologies for intricate data leveraging deep learning techniques. The objective is to address the challenge of proficiently handling and precisely categorizing diverse forms of complex data in practical scenarios. Within this study, a composite model has been devised, merging the functionalities of Convolutional Neural Networks (CNN) with Recurrent Neural Networks (RNN), and augmenting the framework with an Attention Mechanism (AM) to bolster the capability of the model in extracting features and classifying intricate datasets. Through extensive experimentation, this paper validates the efficacy and versatility of the proposed model across diverse data types, including images, texts, and audio. The experimental findings demonstrate that, in contrast to traditional machine learning methods, the deep learning model proposed in this study excels at capturing abstract data features, thereby enhancing classification accuracy and generalization capability. The research presented herein offers novel insights and methodologies for the domain of statistical feature extraction and classification of complex data, thus playing a pivotal role in advancing the practical applications of deep learning technology. © 2024 IEEE.

关键词： Contrastive learning

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Food Safety Assurance: Crystal Strcture Algorithm with Extreme learning machine for Early Detection of Contaminated Products 3

Food Safety Assurance: Crystal Strcture Algorithm with Extre...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mupparaju, Chinna Babu Nalluri, Mounika Rongali, Aruna Sri Ananthan, Bhuvanesh University of Central Missouri Department of Information Technology Missouri United States Murray State University Department of Information Technology Murray United States University of the Cumberlands Department of Information Technology Williamsburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350372120

The incidents of food safety series directly affect the safety as well as endanger to people's health. To ensure food safety, the significant model is to detect the food quality. An actual food inspection risk prediction procedure is complex because the food protection monitoring data contains nonlinear properties, discreteness and high-dimension. For that reason, this paper present a novel crystal structure algorithm with Extreme learning machine (CS-ELM) for early discovery of contaminated products. The characteristic data is effectively received by utilizing proposed CS-ELM model. For the given input data, an optimal solution are predicted to develop the food process optimization model along with ELM. The comparative study executes the direct comparison among each characteristic information process. For emerging food safety issues identification, huge reliable food information are provided to obtain the prediction value as well as characteristic information. The better and energetic proposed detection model demonstrates food circulation efficiency with labor cost and storage reduction. Compared to the conventional techniques, proposed minimizes the food safety risk and ensuring quality products. © 2024 IEEE.

关键词： Crystal structure

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Development and Application of Online learning System Based on Artificial Intelligence 3

Development and Application of Online Learning System Based ...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Gao, Luyun Jinan Preschool Education College Jinan China National University Manila ManilaPH 1008 Philippines

ISBN: (纸本)9798350318609

With the continuous progress of subject knowledge such as Internet and computer technology, as well as the update and improvement of educational models and methods, people's cognition of the concept of artificial intelligence is getting deeper and deeper. Based on the current intelligent background, aiming at various types of problems involved in virtual reality, this paper uses machine automation algorithm to apply online learning system to real life, and studies the optimization of online learning system by artificial intelligence algorithm. After that, this paper also conducts related tests on the performance of the learning system, and the test results show that the resource utilization rate of the system is above 0.7, the data throughput is large, the stability is relatively good, and it is safe enough to run and meet the needs of users. © 2024 IEEE.

关键词： learning systems

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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Water Quality Prediction and Suggesting Filter using machine learning 3

Water Quality Prediction and Suggesting Filter using Machine...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chaudhari, Archana Mane, Rutuja Khot, Anant Kadam, Anup Rajam, Nagraj Department of Instrumentation Engineering Vishwakarma Institute of technology Bibwewadi Maharashtra Pune India

ISBN: (纸本)9798331527549

Survival of all living beings and efficient working of industries and agriculture are largely dependent on the availability of clean water. However, contamination is a major issue that requires dependable methods for assessing the quality of water before its use in drinking, chemical applications, or animal hydration. This research utilizes machine learning to predict water quality based on parameters such as pH, turbidity, hardness, conductivity, and dissolved particles. Based on a broad dataset, models such as time-series analysis and Random Forest classifier are applied to categorize water as safe or unsafe for domestic use. A hardware system equipped with sensors captures real-time data, which enables dynamic assessment of water quality. The proposed solution shows high accuracy in its evaluation using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Regression Analysis, offering a practical approach to ensure water safety in diverse applications. © 2025 IEEE.

关键词： artificial intelligence data visualization dataset filter hardware machine learning preprocessed data Random forest algorithm TDS water quality

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